The healthcare sector is rapidly embracing artificial intelligence (AI), from predictive diagnostics and clinical decision support to patient engagement and operational automation. With this innovation comes an urgent question: How do we ensure these tools are used ethically, securely and in compliance with evolving regulations?
The promise of AI is clear, but so are the risks. AI can amplify bias, disrupt internal controls, and create regulatory exposure without intentional oversight. The time for compliance and risk leaders to get involved is before problems emerge.
“We’re seeing healthcare organizations racing to adopt AI without fully understanding the implications,” says Lance Mehaffey, Senior Director and Healthcare Vertical Leader at NAVEX. “When compliance isn’t at the table from day one, you’re not managing innovation – you’re managing fallout.”
The compliance blind spots AI can create
AI’s ability to parse data and identify patterns is unmatched – but when fed biased or incomplete information, it can perpetuate inequities and make high-stakes decisions with little transparency. Common risk areas include:
- Bias and discrimination in clinical algorithms
- Data integrity challenges across fragmented systems
- Overreliance on outputs without human oversight
- Regulatory gaps, with DOJ, HHS and state authorities racing to catch up
Healthcare organizations that fail to identify and address these blind spots could face operational setbacks and reputational and legal consequences.
Governance matters: Why compliance must lead
The solution isn’t to avoid AI – it’s to govern it. AI governance is quickly becoming a required pillar of enterprise compliance programs.
- Cross-functional collaboration between IT, legal, compliance and operations
- Formal AI impact assessments that evaluate ethical, legal and operational risk
- Ongoing monitoring and auditing of algorithm performance
“Compliance professionals are uniquely positioned to bridge the gap between technical teams and patient safety,” explains Clivetty Martinez, Ph.D., Senior Advisor at Granite GRC. “When you operationalize AI oversight through a GRC lens, you move from firefighting to foresight.”
Risk assessments: The first line of defense
AI-specific risk assessments help organizations uncover where AI is already in use – formally or informally – and whether adequate safeguards are in place. These assessments should consider:
- Use case validity and clinical justification
- Data source quality and integrity
- Privacy implications and HIPAA alignment
- Controls for continuous monitoring and auditability
Assessments aren’t a one-time activity – they’re a living process that evolves with technology.
A healthcare-specific approach to AI oversight
Unlike other industries, healthcare brings layered regulatory obligations, life-or-death consequences and a uniquely complex data environment. Any AI governance program must account for:
- HIPAA compliance and patient confidentiality
- Medical device and vendor risk
- Care delivery standards and reimbursement protocols
- Clinical outcomes and patient equity
“Governance models built for other sectors won’t cut it in healthcare,” says Jeffrey B. Miller, Esq., Director-in-Charge at Granite GRC. “We need risk and compliance strategies that reflect the unique demands of this environment – and that means tailoring our approach from the ground up.”
